Design and build scalable and efficient ML models using both classic and generative AI techniques.
Serve as the go-to expert for all machine learning-related inquiries, providing guidance and mentorship to other team members.
Advise and guide teams on how to deploy sophisticated and scalable ML models and pipelines on Google Cloud Platform, leveraging services such as Vertex AI, BigQuery ML, and AI Platform.
Conduct in-depth research to stay at the forefront of the rapidly evolving fields of machine learning and artificial intelligence.
Collaborate with cross-functional teams to identify opportunities for leveraging machine learning to drive innovation and solve critical business and operational problems.
Ensure the robustness, fairness, and ethical implications of all developed models.
Effectively communicate complex technical concepts to both technical and non-technical stakeholders.

Extensive experience in classic machine learning methodologies, including but not limited to, regression, classification, clustering, and ensemble methods.
Proven experience with generative AI models and techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and large language models (LLMs).
Demonstrated proficiency with Google Cloud Platform (GCP) and its suite of machine learning services.
Strong programming skills in Python and familiarity with common ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Google Professional Machine Learning Engineer certification is highly desirable.
A background working with the United States Air Force or other Department of Defense entities is a significant plus.
A strong track record in research, evidenced by publications or contributions to research projects.
Familiarity with MLOps principles and practices for the continuous delivery and automation of machine learning models.
Excellent problem-solving skills and the ability to work independently and in a team environment.

AI Subject Matter Expert – AI SME

Key skills